Production of cameras with reduced rejection rate

Abstract

A method for producing a camera. The method includes: providing prefabricated components; adjusting at least two of these prefabricated components relative to one another in accordance with at least one specified optimality criterion; and adhesively bonding the components to one another in the adjusted state; wherein prior data characterizing a specific specimen of at least one of the prefabricated components, and/or measured data in respect of the optical performance of the combination of the components adjusted with respect to one another, are mapped by a trained machine learning model onto a prediction for the optical performance that the camera will deliver once it has run through at least one additional production step after the adhesive bonding; and this prediction is used as feedback for an influencing action on the production process.

1 - 12 . (canceled) 
     
     
         13 . A method for producing a camera, comprising the following steps:
 providing prefabricated components;   adjusting at least two of the prefabricated components relative to one another in accordance with at least one specified optimality criterion; and   adhesively bonding the at least two of the prefabricated components to one another in the adjusted state;   wherein:
 prior data characterizing a specific specimen of at least one of the prefabricated components, and/or measured data in respect of an optical performance of the combination of the at least two of the prefabricated components adjusted relative to one another, are mapped by a trained machine learning model onto a prediction for an optical performance that the camera will deliver once the camera has run through at least one additional production step after the adhesive bonding; and 
 the prediction is used as feedback for an influencing action on the production process. 
   
     
     
         14 . The method according to  claim 13 , wherein:
 several candidate specimens are provided for at least one of the at least two of the prefabricated components;   using prior data characterizing each candidate specimen of the candidate specimens, the machine learning model ascertains a respective prediction for an optical performance of a camera which contains the candidate specimen; and   a combination of the candidate specimens for which the prediction satisfies a specified criterion, is selected for further production of the camera.   
     
     
         15 . The method according to  claim 13 , wherein:
 during the adjustment, the machine learning model ascertains multiple times, based on measured data relating to optical performance of a combination of the at least two of the prefabricated components in a current spatial arrangement relative to each other, a prediction for the optical performance of the camera which results when the at least two of the prefabricated components are adhesively bonded to one another in this arrangement; and   in response to the prediction satisfying a specified criterion the at least two of the prefabricated components are adhesively bonded to one another.   
     
     
         16 . The method according to  claim 15 , wherein, during the adjustment, optimization with respect to the prediction provided by the machine learning model is given priority over optimization with respect to the specified optimality criterion. 
     
     
         17 . The method according to  claim 13 , wherein, in response to the prediction for the optical performance of the camera satisfying a specified criterion, the production process is terminated. 
     
     
         18 . The method according to  claim 13 , wherein the prior data characterize:
 a modulation transfer function (MTF) of an optical component, and/or   measurement results from a quality test of a component in the context of prefabrication, and/or   a supplier of a component, and/or   at least one tool used for the production of a component.   
     
     
         19 . The method according to  claim 13 , wherein the measured data characterize:
 a modulation transfer function of a combination of the at least two of the prefabricated components adjusted relative to one another, and/or   dimensions of a spatial arrangement of the at least two of the prefabricated components adjusted to one another.   
     
     
         20 . The method according to  claim 13 , wherein the prediction for the optical performance characterizes a modulation transfer function of the camera as completed. 
     
     
         21 . A non-transitory machine-readable data medium on which is stored a computer program including machine-readable instructions for producing a camera, the instructions, when executed by one or more computers, cause the one or more computers in combination of a production facility for cameras controller by the one or more computers, to perform the following steps:
 providing prefabricated components;   adjusting at least two of the prefabricated components relative to one another in accordance with at least one specified optimality criterion; and   adhesively bonding the at least two of the prefabricated components to one another in the adjusted state;   wherein:
 prior data characterizing a specific specimen of at least one of the prefabricated components, and/or measured data in respect of an optical performance of the combination of the at least two of the prefabricated components adjusted relative to one another, are mapped by a trained machine learning model onto a prediction for an optical performance that the camera will deliver once the camera has run through at least one additional production step after the adhesive bonding; and 
 the prediction is used as feedback for an influencing action on the production process. 
   
     
     
         22 . One or more computers comprising:
 a non-transitory machine-readable data medium on which is stored a computer program including machine-readable instructions for producing a camera, the instructions, when executed by the one or more computers, cause the one or more computers in combination of a production facility for cameras controller by the one or more computers, to perform the following steps:
 providing prefabricated components; 
 adjusting at least two of the prefabricated components relative to one another in accordance with at least one specified optimality criterion; and 
 adhesively bonding the at least two of the prefabricated components to one another in the adjusted state; 
 wherein:
 prior data characterizing a specific specimen of at least one of the prefabricated components, and/or measured data in respect of an optical performance of the combination of the at least two of the prefabricated components adjusted relative to one another, are mapped by a trained machine learning model onto a prediction for an optical performance that the camera will deliver once the camera has run through at least one additional production step after the adhesive bonding; and 
 the prediction is used as feedback for an influencing action on the production process. 
 
   
     
     
         23 . A production facility for cameras, the production facility configured to:
 provide prefabricated components;   adjust at least two of the prefabricated components relative to one another in accordance with at least one specified optimality criterion; and   adhesively bond the at least two of the prefabricated components to one another in the adjusted state;   wherein:
 prior data characterizing a specific specimen of at least one of the prefabricated components, and/or measured data in respect of an optical performance of the combination of the at least two of the prefabricated components adjusted relative to one another, are mapped by a trained machine learning model onto a prediction for an optical performance that the camera will deliver once the camera has run through at least one additional production step after the adhesive bonding; and 
 the prediction is used as feedback for an influencing action on the production process.